用大模型动态优化图结构，提升社交文本人格检测精度
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection
- 用大模型提取语义特征，动态构建和优化图结构
- 在两个数据集上超越现有最优模型，显著提升检测效果
- 适合需要高精度人格分析的社交计算与心理研究场景
基于图的人格检测通过文本数据（如社交媒体）构建图结构。现有方法常受限于稀疏或噪声数据，且依赖静态图，难以捕捉节点间动态关系。本文提出LL4G，一种利用大语言模型（LLMs）自监督优化图神经网络（GNNs）的框架。LLMs提取丰富语义特征生成节点表示，并推断显性与隐性关系。图结构根据输入数据自适应增删节点与边，持续优化。GNN联合训练节点重建、边预测与对比学习任务，融合语义与结构信息，生成鲁棒人格画像。在Kaggle与Pandora数据集上的实验表明，LL4G优于当前最优模型。
原文摘要 · Abstract (English)
Graph-based personality detection constructs graph structures from textual data, particularly social media posts. Current methods often struggle with sparse or noisy data and rely on static graphs, limiting their ability to capture dynamic changes between nodes and relationships. This paper introduces LL4G, a self-supervised framework leveraging large language models (LLMs) to optimize graph neural networks (GNNs). LLMs extract rich semantic features to generate node representations and to infer explicit and implicit relationships. The graph structure adaptively adds nodes and edges based on input data, continuously optimizing itself. The GNN then uses these optimized representations for joint training on node reconstruction, edge prediction, and contrastive learning tasks. This integration of semantic and structural information generates robust personality profiles. Experimental results on Kaggle and Pandora datasets show LL4G outperforms state-of-the-art models.
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